Public Interest Energy Research (PIER) Program INTERIM PROJECT REPORT HUMBOLDT COUNTY AS A RENEWABLE ENERGY SECURE COMMUNITY Resource and Technology Assessment Report Appendices - DRAFT Prepared for: California Energy Commission Prepared by: Schatz Energy Research Center, Humboldt State University AUGUST 2012 CEC-XXX-2012- Prepared by: Primary Author(s): Jim Zoellick, Senior Research Engineer, SERC Colin Sheppard, Research Engineer, SERC Peter Alstone, Research Engineer, SERC Also Contributing: Andrea Alstone, Dr. Charles Chamberlin, Ruben Garcia, Dr. Steven Hackett, Dr. Peter Lehman, Eric Martin, Luke Schiedler, SERC Schatz Energy Research Center Humboldt State University Arcata, CA 95521 707-826-4345 www.schatzlab.org Contract Number: PIR-08-034 Prepared for: Public Interest Energy Research (PIER) California Energy Commission Michael Sokol Project Manager Linda Spiegel Office Manager Energy Generation Research Office Laurie ten Hope Deputy Director Energy Research & Development Division Robert P. Oglesby Executive Director DISCLAIMER This report was prepared as the result of work sponsored by the California Energy Commission. It does not necessarily represent the views of the Energy Commission, its employees or the State of California. The Energy Commission, the State of California, its employees, contractors and subcontractors make no warrant, express or implied, and assume no legal liability for the information in this report; nor does any party represent that the uses of this information will not infringe upon privately owned rights. This report has not been approved or disapproved by the California Energy Commission nor has the California Energy Commission passed upon the accuracy or adequacy of the information in this report. Copyright © 2012 Schatz Energy Research Center APPENDIX A: Electric Vehicle Assessment A.1 Analysis Methodology A.1.1 Baseline To assess the impacts that electric vehicles (EVs) could have in Humboldt County it was first necessary to characterize the existing vehicle fleet as a basis for comparison. To establish such a baseline, county level transportation data were collected from the California Department of Motor Vehicles and the California Department of Transportation. Using these data, registered vehicles, annual vehicle miles traveled, and annual vehicle fuel consumption were extrapolated to estimate future growth. National fuel economy estimates by the Bureau of Transportation Statistics were also used to estimate future baseline fleet vehicle fuel consumption in Humboldt County. An annual vehicle fuel consumption growth rate of 0.5 percent was used based on average annual vehicle fuel consumption growth from 1999 to 2006. This suggests that about 76.3 and 80.2 million gallons of fuel would be consumed in 2020 and 2030, respectively. This corresponds to an average of 11,000 miles of travel per vehicle-year. A.1.2 Vehicle Adoption Rate To project the adoption of electric vehicles in Humboldt County, two primary literature sources were consulted. This included the Environmental Assessment of Plug-In Hybrid Electric Vehicles by EPRI and the Natural Resource Defense Council (EPRI/NRDC, 2007) as well as the Long-Range EV Charging Infrastructure Plan for Western Oregon by the Electric Transportation Engineering Corporation (ETEC, 2010). Integral to the EPRI/NRDC study was the development of market adoption scenarios, which estimate the new vehicle market share of plug-in hybrid electric vehicles from 2010 to 2050. Low, Medium, and High scenarios were developed and achieve a market share of 20 percent, 51 percent, and 68 percent, respectively, by 2030, and a maximum market share of 20 percent, 62 percent, and 80 percent, respectively, by 2050 (Figure 25). In comparison to other projections in the literature, EPRI’s Medium and High plug-in hybrid electric vehicle scenarios are aggressive. The underlying assumptions for these projections are not specifically outlined, though they are intended to apply at the national level. As Humboldt County is a rather isolated rural area in Northwestern California, it is questionable how well these penetration rates may apply. State or regional market share projections may be more applicable. The Electric Transportation Engineering Corporation study (ETEC, 2010) was also consulted. In August of 2009, ETEC was awarded a $99.8 million grant from the U.S. Department of Energy to partner with Nissan to deploy up to 4,700 battery electric vehicles (BEVs, for example, Nissan Leafs) and 11,210 charging systems in strategic markets in the states of Arizona, California, Oregon, Tennessee, and Washington (ETEC, 87 2010). The ETEC study presents various electric vehicle penetration projections for the U.S. and Western Oregon. Given Humboldt County’s proximity and demographic similarities to the Corvallis-Albany region cited in the ETEC report, electric vehicle market share projections for the Corvallis-Albany area were assumed applicable and scaled to Humboldt County. Figure 25: Assumed New Vehicle Market Share Under a Medium Plug-In Hybrid Electric Vehicle Scenario Source: EPRI (2007) Figure 26 shows the projected electric vehicle market share for Corvallis-Albany in comparison to plug-in hybrid electric vehicles market share projected for the U.S. according to the aforementioned EPRI scenarios. The ETEC Corvallis scenario most closely follows the EPRI Low scenario. Doubling the Corvallis scenario penetration rates achieves a 2030 market penetration (53 percent) similar to that of the EPRI Medium scenario (51 percent), but is much slower in reaching that point. Figure 27 shows cumulative electric vehicles in Humboldt County according to the aforementioned electric vehicles market share projections. Three electric vehicle penetration scenarios were assumed for Humboldt County: (1) A likely (medium) scenario corresponding to expected electric vehicle diffusion in Corvallis-Albany; (2) an optimistic (high) scenario corresponding to a doubling of the expected penetration in Corvallis-Albany; and (3) a maximum scenario corresponding to the EPRI Medium scenario, meant to serve as a cap for electric vehicles penetration in the REPOP Model. This resulted in a cap on cumulative vehicle penetration in 2030 of 38 percent. 88 Figure 26: Assumed New Car Market Share in the United States for Low, Medium, and High Plug-in Hybrid Electric Vehicle Scenarios and Corvallis-Albany EV Scenario 70% 60% 50% 40% 30% 20% % of New Vehicle Sales 10% 0% 2010 2015 2020 2025 2030 ETEC: Corvallis ETEC: Corvallis (x2) EPRI: Low EPRI: Medium EPRI: High Source: SERC compilation from EPRI (2007) and ETEC (2010) Figure 27: Projected Cumulative EVs in Humboldt County Based on Low, Medium, and High Plug-in Hybrid Electric Vehicle Scenarios and a Corvallis-Albany EV Scenario 90,000 80,000 70,000 60,000 50,000 40,000 Cumulave EVs 30,000 20,000 10,000 0 2010 2015 2020 2025 2030 ETEC: Corvallis ETEC: Corvallis (x2) EPRI: Low EPRI: Medium EPRI: High Source: SERC compilation from EPRI (2007) and ETEC (2010) 89 A.1.3 Vehicle Performance Most assumptions about electric vehicle architecture, performance, energy consumption, and capital and maintenance costs were obtained from the literature source Where Are the Market Niches for Electric Drive Vehicles by Santini et al. (2010), as well as via personal communication with Bryan D. Jungers, Research Manager at E Source in Boulder, Colorado. Only five electric vehicles were considered for this analysis based on architectures that are representative of current or near market technologies (Table 15). Santini et al. (2010) used the Powertrain System Analysis Toolkit to model and evaluate the performance of nine electric vehicles spanning the major classes of electric drive vehicles, including the split parallel-and-series system used by Toyota and Ford, a series extended range electric vehicle (EREV) similar to the Chevrolet Volt, and a battery electric powertrain similar to the Nissan Leaf,, Table 16.. Table 15: Description of Conventional and Electric Vehicles Considered for Adoption Vehicle Description Conventional vehicle; standard mid-size sedan powered by an internal Conventional combustion engine (for example, Toyota Camry) Plug-in hybrid electric vehicle with a power-split drivetrain and an all- PHEV10 electric range of 10 miles (for example, Toyota Plug-in Prius) Plug-in hybrid electric vehicle with a series drivetrain and an all-electric PHEV40 range of 40 miles Extended range electric vehicle; a plug-in hybrid electric vehicle with a series powertrain and an all-electric range of 30 miles; EREVs and series PHEVs are virtually identical, except that an EREV has an over-sized electric motor and battery that enable aggressive acceleration without EREV30 triggering an engine-on event Extended range electric vehicle; a plug-in hybrid electric vehicle with a series powertrain and an all-electric range of 40 miles (for example, the Chevrolet Volt was originally advertised as an EREV40, but is reportedly EREV40 a PHEV40 with a power-split drivetrain) Battery electric vehicle with an all-electric range of 100 miles (for BEV100 example, Nissan Leaf) Source: SERC staff. 90 Table 16: Drivetrain Components of Vehicles Considered for Adoption Gross MC2/Gen MC2/Gen ICE MC Peak MC Cont. Component Vehicle Peak Cont. Power Power Power Weight Power Power Vehicle Name (kg) (kW) (kW) (kW) (kW) (kW) Conventional 2111 135 0 0 2 2 PHEV10 2201 80 76 38 45 30 PHEV40 2321 70 108 54 68.25 68.25 EREV30 2325 75 109 54.5 73.2 73.2 EREV40 2355 75 109 54.5 73.2 73.2 BEV100 2185 0 103 51.5 0 0 BOL ESS EOL ESS BOL ESS EOL ESS CD Total Total Power-to- Power-to- Degree- Component Useable Nominal Nominal Energy Energy of-Elec. Energy Energy Energy Ratio Ratio Vehicle Name (kWh) (kWh) (1/hr) (1/hr) (%) (kWh) Conventional 0.0 0.0 0.0 0.0 0% 0.00 PHEV10 5.0 4.0 15.5 15.5 41% 2.30 PHEV40 16.9 13.5 4.4 4.4 44% 8.96 EREV30 12.4 9.9 15.4 15.4 59% 6.42 EREV40 16.3 13.1 12.1 12.1 59% 8.64 BEV100 33.2 26.6 5.8 8.3 100% 18.6 Component Definitions ICE: internal combustion engine MC: electric machine/motor MC2: electric machine/motor #2 Gen: generator BOL: beginning of life EOL: end of life ESS: energy storage system CD: charge-depleting Source: Santini et al.